独立分量分析在机械振动信号上的应用研究

Study on application of independent component analysis in mechanical vibration signals

独立分量分析在机械振动信号的特征提取上能起到重要作用。首先建立以信息论为框架的独立性判据和优化算法,然后给出衡量独立分量分析分离性能的指标,这样建立的优化算法能很好地分离出混合信号。最后对两个亚高斯信号和一个超高斯信号的混合信号进行仿真实验。仿真结果表明,灵活的ICA算法分离效果要好于随机梯度算法的分离效果,该信号分析方法具有收敛性好,误差小的优点。

The independent component analysis（ICA）plays an important role in extracting characteristics of mechanical vibration signals.First independence criterion and optimization algorithms were established based on information theory as a framework in it.Then an index for measuring separation performance of independent component analysis was given,which optimization algorithm built can extract mixed signals very well.Finally,mixed signal from two Gauss signals and a Gauss signal were simulated,which results indicate that,the extracting effect of flexible ICA algorithm is better than that of the stochastic gradient algorithm,and the signal analysis method is characterized with its better convergences and less errors.